Research Article Open Access

Computer Machine Vision Inspection on Printed Circuit Boards Flux Defects

Ang TeohOng1, Zulkifilie Bin Ibrahim2 and Suzaimah Ramli3
  • 1 Control Ez Technology Sdn. Bhd, No.4-4, Jalan SP2/2, Malaysia
  • 2 Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
  • 3 Universiti Pertahanan Nasional Malaysia, Malaysia


The new visual inspection systems techniques using real time machine vision replace the human visual manual inspection on PCB flux defects, which brings harmful effects on the board which may come in the form of corrosion and can cause harm to the assembly. In short, it brings improvement in Printed Circuit Boards (PCB) production quality, principally concerning the acceptance or rejection of the PCB boards. To develop new algorithm in image processing which detects flux defect at PCB board during re-flow process and achieve good accuracy of the PCB quality checking. The machine will be designed and fabricated with the total automation control system with mechanical PCB loader/un-loader, pneumatic system handler with vacuum cap, vision inspection station and final classification station (accept or reject). The image processing system is based on shape (pattern) and color image analysis techniques with Matrox Imaging Library. The shape/texture of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. The color analysis of the flux defect in a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurring on the PCB flux defects. The system was tested with PCB boards from factory production line and achieved PCB board flux defects sorting accuracy at 86.0% based on proposed pattern matching technique combined with red color filter band histogram.

American Journal of Engineering and Applied Sciences
Volume 6 No. 3, 2013, 263-273


Submitted On: 8 July 2013 Published On: 28 August 2013

How to Cite: TeohOng, A., Ibrahim, Z. B. & Ramli, S. (2013). Computer Machine Vision Inspection on Printed Circuit Boards Flux Defects. American Journal of Engineering and Applied Sciences, 6(3), 263-273.

  • 3 Citations



  • Machine Vision
  • Pattern Matching Technique
  • RGB Color Model
  • Red Color Filter Band
  • Threshold
  • Histogram